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Social desirability bias is a problem in surveys collecting data on private issues, deviant behavior or unsocial opinions (e.g. sex, health, income, illicit drug use, tax evasion or xenophobia) as soon as the respondents’ true scores differ from social norms. Asking sensitive questions poses a dilemma to survey participants. On the one hand, politeness norms may oblige the respondent to be helpful and cooperative and self-report the sensitive personal information truthfully. On the other hand, the respondent may fear negative consequences from self-reporting norm-violating behavior or opinions within a survey setting. Cumulative empirical evidence shows that in the context of surveying sensitive issues respondents often engage in self-protective behavior, i.e. they give socially desirable answers or they refuse to answer at all. Such systematic misreporting or nonresponse leads to biased estimates and poor data quality of the entire survey study. Specific data collection approaches have been proposed to increase respondents’ cooperation and improve validity of self-reports in sensitive surveys. Furthermore, in recent years, web and mobile web technologies as well as big data approaches offer new (non-reactive) perspectives in gathering data on sensitive topics and in tackling social desirability bias.

This session aims at deepening our knowledge of the data generation process and advancing the theoretical basis of the ongoing debate about establishing best practices and designs for surveying sensitive topics. We invite submissions that deal with these problems and/or present potential solutions. In particular, we are interested in studies that (1) reason about the psychological processes and social interactions between the actors that are involved in the collection of the sensitive data; (2) present current empirical research focusing on ‘question-and-answer’ based (e.g. randomized response and item count techniques, factorial surveys and choice experiments), non-reactive (e.g. record linkage approaches, big data analyses, field experiments, or administrative data usage) or mixed methods of data collection (e.g. big data analyses in combination with classical survey approaches) focusing on the problem of social desirability and highlighting best practices regarding recent methodological and technological developments; (3) deal with statistical procedures to analyze data generated with special data collection methods; (4) explore the possibilities and limits of integrating new and innovative data collection approaches for sensitive issues in well-established, large-scale population surveys taking into account problems of research ethics and data protection.